Semantic search is a data searching technique in a which a search query aims to not only find keywords, but to determine the intent and contextual meaning of the the words a person is using for search. Semantic search provides more meaningful search results by evaluating and understanding the search phrase and finding the most relevant results in a website, database or any other data repository.
Google Semantic Search: A Brief History
The Knowledge Graph
Introduced in 2012, the Knowledge Graph was Google’s first step in developing the importance of entities and context over strings of keywords – or, as Google phrased it, “things, not strings.” The Knowledge Graph enables users to search for things, people or places that Google knows about — with information gathered from a variety of sources — and instantly get information that’s relevant to your query. The information is presented to users in an Infobox next to the search results.
This is a critical first step towards building the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do.
Hummingbird is the name given to a significant algorithm change in Google Search in 2013. The name comes from being “precise and fast” and is designed to better focus on the meaning behind the words. It looks deeper at the content on individual pages of a website, with improved ability to lead users directly to the most appropriate page rather than just a website's homepage. Hummingbird is the biggest search engine algorithm change ever, it focuses on natural language queries, matching content with user intent over individual keywords — on Semantic Search.
After 15+ years of data and user analysis, as well as testing and tweaking in order to deliver a substantial search experience for Google, Hummingbird was essentially an entirely revamped version of Google’s search algorithm, not just a patch or minor update and it was the most ambitious adjustment of Google’s search algorithm since 2001. At the heart of the Hummingbird lies the all-important concept of Semantic Search, or meaning. Hummingbird was more than a simple algorithm update; at its core, it was a fundamental shift in the way Google would deliver results to their users.
Google realized that Search Engine Results Page (SERP) containing links to numerous pages, through which users could have probably learned the answer, was a slow and often irritating process, even for users who were presented with relevant results. In order to deliver the right results as quickly as possible, Google created semantic search.
Semantic search is the concept of improving search results by focusing on user intent and how the subject of a search relates to other information in a wider sense, or its contextual relevance. In short: semantic search is the quest to provide relevant results based on user intent and context. Essentially, semantic search focuses on determining what a user really means, rather than a string of keywords, and then serving relevant results.
The goal of semantic search is to leverage the massive amount of data that Google has collected to deliver contextually appropriate answers to the world’s questions. Here are three core elements that made this shift so unique:
It takes the entire query into account, not just the keywords.
It takes the user, their search patterns, history and other variables into account.
It takes into account the device type, time of day and location.
At its core, the purpose of semantic search is to create a relational connection by fast delivering contextualized content. It is paying more attention to each word in a query, ensuring that the whole query — the whole sentence or conversation or meaning — is taken into account, rather than particular keywords. Semantic search is about queries, not keywords. The goal is that pages matching the meaning do better, rather than pages matching just a few words.
Google has implemented semantic search into its core algorithm by the introduction of Hummingbird.
"You say tomato, I say tomahto"..
Google has been working with Synonyms for a long time. If we look at the timeline Google itself shared in its 15th anniversary post, it has used them since 2002, even though we can also tell that disambiguation (meant as orthographic analysis of the queries) has been applied since 2001.
But only relying on synonyms was not a perfect solution, because two words may be synonyms and may not be so depending on the context they are used. Therefore, in order to deliver the best results possible using semantic search, what Google needed to understand better, easier, and faster was Context. Hummingbird is how Google solved that need.
Speaking literally, words are not "things" themselves but the verbal representation of things, and Search Entities are how Google objectifies words into concepts. An object may have a relationship with others that may change depending on the context in which they are used together. In this sense, words are treated like people, cities, books, and all the other named entities usually related to the Knowledge Graph. The mechanisms Google uses in identifying search entities are especially important in disambiguating the different potential meanings of a word, and thereby refining the information retrieval accordingly to a "probability score."
Third concept playing an explicit role in Hummingbird patent is Co-occurrences. Co-occurrence is a concept which refers to the common presence, frequency of occurrence, and close proximity of similar keywords present across several websites. Co-occurrence may include keywords that are similar to each other and based on the same topic, but are not exactly the same.
Integrating these three elements, Google now is able:
To better understand the intent of a query;
To broaden the pool of web documents that may answer that query;
To simplify how it delivers information, because if query A, query B, and query C substantively mean the same thing, Google doesn't need to propose three different SERPs, but just one;
To offer a better search experience, because expanding the query and better understanding the relationships between search entities, also based on direct/indirect personalization elements, Google can now offer results that have a higher probability of satisfying the needs of the user.
As a consequence, Google may present better SERPs also in terms of better ads, because verbose queries were not presenting ads in their SERPs before Hummingbird.
Google search is the most widely used big data tool in the world. To optimize our sites, we need to have a basic understanding of data entities. Data entity refers to any person, place or thing data can represent or any data classification that links to other data classifications in relationships. When looking to make sense of your site and content, Google not only compares the entities on your site with each other, they also compare your site with trustworthy and authoritative sites across the web with the same entities.
Alexis Sanders gave an awesome explanation of semantic search in the form of Simpsons family. The figure helps us to understand what Google sees as entities and how Google identifies the attributes and the relationships between entities.
Hence, when you search on Google with the query “What is the name of Simpsons dog?”, Google returns a direct answer – “Laddie”.
Google Semantic Search and SEO
Many experts speculate that developments in the field of natural language processing – the process by which machines can effectively parse and interpret human speech – will become a driving force in the advancement of semantic search.
While there was a significant shift in the way results are delivered, the basics of SEO still apply. Links are still important, keywords still matter, on-page optimization is still essential. When things change, it’s easy to assume “what was” no longer works. But what semantic search has really done is made it harder for those who use less-than-ethical tactics to rank.
Optimizing pages and sites for Google Hummingbird is simple. Okay, maybe not that simple, but it really is pretty straightforward. "All we have" to do is create great content that your audience wants and finds useful in conversational way, and enriches their overall experience. In short, we must cover:
The relationships between words.
We know that long-form content can work exceptionally well as part of a wider content strategy, but if every single post you publish is a 4,000-word monster, you may not be meeting all of your readers’ needs. For this reason, mixing up the length of content. intersperse shorter articles among longer ones and include visual content breaks would be the best content strategy.
Sometimes people don’t want to read an article of any length, at all. This is when visual content shines. Infographics, videos and even simple visual elements such as charts and graphs can add some much-needed spice to your content. In addition, they’re often easily skimmable, can picture highly complex ideas effectively and bring some life and colors to your site. Easy, right?
Something that some sites fail to take advantage of is using industry-appropriate language in their content. This is sometimes done out of a fear of alienating potential readers who may not be familiar with a certain topic or area. However, writing content that includes appropriate terminology can demonstrate to Google that your site is authoritative and valuable.
Simply put, if Google and other semantic search engines are going to serve our needs, they need to understand what we’re saying and talking about, the context in which we want our information, and where-and-when we want it. Hummingbird allows the Google search engine to better do its job through an improvement in semantic search. As conversational search becomes the norm, Hummingbird lends understanding to the intent and contextual meaning of terms used in a query. As Hummingbird uses phrases, rather than keywords, the use of long-tail keywords are likely to become more important than ever in SEO.
But what does conversational search look like? It means searching like you talk. People communicate with each other by conversation, not by typing keywords — and it's been a challenge to make Google understand and answer questions more like people do. With semantic search, Google is moving from “strings to things.” This means it wants to create more natural connections. Hummingbird is even more intelligent, it recognizes keyword stuffing and issues penalties accordingly.
But here’s the kicker: The algorithm cannot derive meaning and understanding on its own. We seem to forget that it’s an algorithm and not a human. Like any data-driven program or artificial intelligence, it needs structured data to learn. When we hear terms like artificial intelligence and machine learning, it is easy to think of self-educating robots that seek out information on their own. This is not how Hummingbird, RankBrain or any other aspect of the algorithm works. It requires education to become intelligent.
Please remember that it is a mistake to think that search algorithm knows more than we do. When Google tries to solve search intent, it uses tons of previously collected end user data to understand what users find relevant. Thus, it needs data to learn and data to guide it once it has landed on your site.
Since Hummingbird is more about search entities, better information retrieval and query expansion, we should stop focusing only on keyword optimization and start working on topical context optimization that meets user intent and is fully search-engine-crawler-friendly.
6 Ways to Optimize for Semantic Search
Provide value. Write in natural and semantically rich language.
Pursue search intent with semantic keyword groups .
Develop targeted content around your users' intent that answers your customer’s questions. Structure sentences clearly and answer-based.
Create topic clusters through managing your site structure and internal linking.
Create entities and clear structured architecture with a landing page-centric strategy in mind — become Knowledge Bar entity.
Amplify your content.
From the perspective of search engines, it is easy to understand why they pursue the development of semantic search. It means more data, deeper understanding of natural language and search intent, following with more relevant results and less spam.
The main goal is to give the user the best experience possible and offer Google easily understandable context for the topics around which we have created a page. You still have to follow the best SEO practices, like keyword research, link building, enhancing UX, and so on and so forth. The only difference is that we need to do it with the thought in mind that you optimize content and user intent together with strong technical search engine optimization.
Search engines are incorporating semantic signals in their results.
Semantic search provides additional meaning for engines: data, spam, answering user questions, establishing more personalized results, and providing a more conversational user experience.
Semantic search high-level strategies: Provide value to your visitor, answer your customers' questions, create content with structured sentences, and implement structured data.
All of these work together in order to provide the best search experience possible and if we can get the balance right, we are in Business. Keep up the good work, refine it, be creative and engaging. Quality and Integrity is everything that we do Online.
Hummingbird has been cited as a complete overhaul of the Google core algorithm.
#google #hummingbird #algorithm #semantic #search #seo
🎯 Next time: The breakdown is Do, Know, Go for Semantic SEO Searches.
We cannot keep up with the algorithms at this point, the only thing we can keep up with is the technology and understanding how users interact with it. You can’t win by trying to game an algorithm that’s increasingly based on machine learning, but what you can do is understand the goal and build your quality content internet presence toward that. Start using these strategies today. After all, you will only be able to rank tomorrow if you prepare today.
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